TRIMER: Transcription Regulation Integrated with Metabolic Regulation

Abstract

Summary There has been extensive research in predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes arising from interactions among different biomolecules. For more accurate and robust prediction of target metabolic behavior under different conditions, not only metabolic reactions but also the genetic regulatory relationships involving transcription factors (TFs) affecting these metabolic reactions should be modeled. We have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with Metabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptomes to model the transcription factor regulatory network. TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors at the genome scale. We demonstrate TRIMER’s applicability to both simulated and experimental data and provide performance comparison with other existing approaches.

Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline

“…Demo MATLAB code illustrating the complete procedure of applying TRIMER for biomass and indole flux prediction with TF-knockouts are accessible at the following paths:

>TRIMER\flux_biomass.m, >TRIMER\flux_indole.m.

Figure 3 TRIMER workflow A workflow summarizing the interconnections between the main computational modules in TRIMER (Modified based on two figures in the original publication ( Niu et al., 2021 ), which is an open access article under the CC BY-NC-ND license: http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

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“… A workflow summarizing the interconnections between the main computational modules in TRIMER (Modified based on two figures in the original publication ( Niu et al., 2021 ), which is an open access article under the CC BY-NC-ND license: http://creativecommons.org/licenses/by-nc-nd/4.0/ ). …”

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Figure 6 TRIMER metabolic network analysis data structure The data structure used to represent flux constraints: Gray areas represent the information obtained from the metabolic model; white areas represent the information of additional flux constraints added by users (Modified based on two figures in the original publication ( Niu et al., 2021 ), which is an open access article under the CC BY-NC-ND license: http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

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“…With the advent of high-throughput profiling technologies, genome-scale gene expression profiles can be easily obtained to help infer genetic regulatory rules. The recently developed hybrid model, Transcription Regulation Integrated with MEtabolic Regulation (TRIMER) ( Niu et al., 2021 ), motivated by PROM ( Chandrasekaran and Price, 2010 ), employs a Bayesian Network inferred from the gene expression data, built with the prior knowledge on regulatory relationships between TFs and genes to probabilistically model TF-gene regulatory rules. The resulting BN can be used to infer the joint probabilities in more general forms of Pr(gene(s)|TF(s)) than the marginal probabilities in forms of Pr(gene|TF) in PROM.…”

“…For E. coli and yeast, TRIMER achieves reliable knockout phenotype and flux predictions from the deletion of one or more TFs at the genome scale. For complete details on the use and execution of this protocol, please refer to Niu et al. (2021) .…”